Want Big Data Success? Hire a Biologist

Not all data scientists need a math or economics background. It's time to think outside the box come hiring time.

While IT directors immerse themselves in data scientists' resumes, hiring budgets, and team structures with the goal of staying competitive, they may be missing a big piece of the puzzle.

Rather than just having teams of big data specialists who hail from the traditional backgrounds of statistics, theoretical math, applied math, and/or econometrics, a hiring tip for IT directors is to mix things up. Hire outside of the traditional box. That could mean a biologist, a psychologist, or a philosopher -- depending on the business needs and the kind of mind set that complements the other team members.

The promise of big data is alluring -- important, game-changing decisions can be analyzed and made based on quantitative facts that can be proven like never before. And businesses are right to be allocating a bigger part of their budgets to big data.

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But IT directors and other business leaders shouldn’t get so lost in all that data and miss sight of diversity and creativity and different perspectives that have been known to drive innovation. While big data and analytics teams are certainly at the center of this proliferating trend, their successes depend on their ability to derive meaningful insights that ultimately impact the bottom line positively. And to do this, they need to interface effectively across other business divisions like marketing and sales.

Big data is only truly effective for business innovation if you know how to identify unexpected insights within the data. That's the trick question -- how do you make the most of big data without losing out on the secret spice that leads to breakthrough innovations?

For starters, we can look at the typical qualifications for a big data "scientist" who has the traditional background of statistics, theoretical math, applied math, and/or econometrics. Knowing the data scientist teams are full of these skills, IT directors can then look at other skills that the team lacks such as communications, project management, and planning.

I have had some exceptional students from the conservation biology Ph.D. program in my graduate level regression class. Although their statistics backgrounds tend to be weaker than those of our own statistics students, they make up for it with an excellent study ethic (data collection in the Florida swamps as an arduous alternative), an appreciation for temporal and spatial variables (not just generic X's as predictor variables), an inherent grasp of hypotheses and tentative models, and considerable experience in documenting their work. These students are certainly experienced at writing reports and explaining the results in a fashion understandable to the general public (goes with the conservation biology territory I guess). Finally, those students who have collected data themselves in the field, have a healthy appreciation and skepticism for the examples presented in class.

Many folks I speak to say they look for creative people, folks with liberal arts degrees and natural curiousity. While these people can learn big data, their inherent skills will provide them with the keys to success within big data careers. I thought this was interesting, since liberal arts degrees have been panned for so long.

I like the hire a 'wild card' advice here. Some of the smartest CIOs will tell you when you hire, you must find people who are not just like you -- otherwise you will just listen to yourself talk, no one will challenge your ideas. You need diversity of experience in IT teams. In this age of big data, that is only more true. I hope hiring managers will think outside the box a bit. IT hiring has become too bogged down looking for exact matches to crazy requirements lists, don't you think?

@Shane, I agree that this is spot-on advice. To be effective, a data science team working with big data needs people who can do the math and know the computer science and create the models, but the results of their work will only be as good as their ability to communicate them to the business. That's where the insight and innovation that non-traditional data analysts can bring to the team really shine.

Big data is vague (how big?), and also non-specific (the challenges of analysing vast quantities of numerical data on one server are different from those of analysig unstructured data dispersed over a number of servers): but I fear we're stuck with the term.

This is spot on. People from all lines of work are interested in the stories data can tell and the insights and business results it can produce. If we leave all data analysis up to traditional mathematicians and statisticians, we may not see the big picture.

Most IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.

Why should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.